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Identification of the Factors That Influence University Learning with Low-Code/No-Code Artificial Intelligence Techniques

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Escuela de Ingeniería en Tecnologías de la Información, FICA, Universidad de Las Américas, Quito 170125, Ecuador
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Departamento de Sistemas, Universidad Internacional del Ecuador, Quito 170411, Ecuador
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Author to whom correspondence should be addressed.
Academic Editor: Jun Liu
Electronics 2021, 10(10), 1192; https://doi.org/10.3390/electronics10101192
Received: 27 April 2021 / Revised: 11 May 2021 / Accepted: 14 May 2021 / Published: 17 May 2021
Education is one of the sectors that improves the future of societies; unfortunately, the pandemic generated by coronavirus disease 2019 has caused a variety of problems that directly affect learning. Universities have found it necessary to begin a transition towards remote or online educational models. To do so, the only method that guarantees the continuity of classes is using information and communication technologies. The transition in the foreground points to the use of technological platforms that allow interaction and the development of classes through synchronous sessions. In this way, it has been possible to continue developing both administrative and academic activities. However, in effective education, there are factors that create an ideal environment where the generation of knowledge is possible. By moving from traditional educational models to remote models, this environment has been disrupted, significantly affecting student learning. Identifying the factors that influence academic performance has become the priority of universities. This work proposes the use of intelligent techniques that allow the identification of the factors that affect learning and allow effective decision-making that allows improving the educational model. View Full-Text
Keywords: artificial intelligence; remote education; WEKA artificial intelligence; remote education; WEKA
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MDPI and ACS Style

Villegas-Ch., W.; García-Ortiz, J.; Sánchez-Viteri, S. Identification of the Factors That Influence University Learning with Low-Code/No-Code Artificial Intelligence Techniques. Electronics 2021, 10, 1192. https://doi.org/10.3390/electronics10101192

AMA Style

Villegas-Ch. W, García-Ortiz J, Sánchez-Viteri S. Identification of the Factors That Influence University Learning with Low-Code/No-Code Artificial Intelligence Techniques. Electronics. 2021; 10(10):1192. https://doi.org/10.3390/electronics10101192

Chicago/Turabian Style

Villegas-Ch., William, Joselin García-Ortiz, and Santiago Sánchez-Viteri. 2021. "Identification of the Factors That Influence University Learning with Low-Code/No-Code Artificial Intelligence Techniques" Electronics 10, no. 10: 1192. https://doi.org/10.3390/electronics10101192

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